Finer Time Scales Research Articles

Heat and Drought Have Exacerbated the Midday Depression Observed in a Subtropical Fir Forest by a Geostationary Satellite

Recently, increasing heat and drought events have threatened the resilience of Chinese fir forests. Trees primarily respond to these threats by downregulating photosynthesis including through stomatal limitation that causes a drop in productivity at noon (known as the midday depression). However, the effects of these events on midday and afternoon GPP inhibition are rarely analyzed on a fine timescale. This may result in negligence of critical responses. Here, we investigated the impact of climatic events on the midday depression of photosynthesis at a subtropical fir forest in Huitong from 2016 to 2022 using data from the Himawari 8 meteorological satellite and flux tower. Our results indicated that the highest number of midday depression occurred in 2022 (126 times) with the highest average temperature (29.1 °C). A higher incidence of midday depression occurred in summer and autumn, with 48 and 34 occurrences, respectively. Compound drought, heat, and drought events induced increases in midday depression at 74.3%, 66.0%, and 47.5%. Thus, trees are more likely to adopt midday depression as an adaptive strategy during compound drought and heat events. This study can inform forest management and lead to improvements in Earth system models.

Multi-time Scale Variation of Atmospheric Ammonia Concentration and Dry Deposition in a Paddy Rice Region in Subtropical China

Meteorological factors and anthropogenic activities significantly affect atmospheric ammonia （NH3） concentration and its dry deposition. Former studies have examined the spatial and temporal variability in atmospheric NH3 concentrations at monthly scales. However, the characteristics of atmospheric concentrations at finer time scales such as hourly and daily scales and the influencing factors remain unclear. In this study, atmospheric NH3 concentration and related meteorological factors were continuously monitored online for one year in a double cropping rice region in subtropical China, and atmospheric NH3 concentration and its meteorological influencing factors as well as dry deposition were analyzed at different time scales （hourly, daily, and monthly）. The main results were as follows： The annual average daily concentration of NH3 in the rice area varied from 0.01 to 58.0 μg·m-3 （in N, same below）, and the annual average concentration was 5.3 μg·m-3. On the hourly scale, the 24-hour dynamics of atmospheric NH3 concentration showed a unimodal pattern, and the time of the NH3 peak appearance in different seasons was different； the time of the peak that appeared in winter lagged behind that in the other seasons. From the perspective of daily scale, NH3 concentration was mainly affected by fertilization in the paddy fields, peaking at 1-3 days after fertilization and then gradually decreasing. On the monthly scale, NH3 concentration peaked at 12.8 μg·m-3 in July and was the lowest in October at 1.6 μg·m-3. On the hourly scale, NH3 concentration varied seasonally due to the influences of meteorological factors, mainly as follows： NH3 concentration showed significant positive correlations with air temperature and solar radiation in all four seasons and with wind speed in spring and summer, whereas it showed significant negative correlations with relative humidity except in winter. On the daily scale, NH3 concentration showed a significant positive correlation with air temperature, rainfall, and solar radiation, whereas it showed a significant negative correlation with relative humidity. On the monthly scale, no significant correlation existed between each meteorological factor and NH3 concentration. The annual dry deposition flux （in N） calculated from the hourly average NH3 concentration was 8.5 kg·（hm2·a）-1, which was 11.6% higher than the annual flux calculated from the daily average and 12.4% higher than the annual flux calculated from the monthly average. In summary, there were significant daily and seasonal variations in atmospheric NH3 concentration in the paddy rice region in subtropical China, and conducting hourly-scale observations of NH3 concentration can help to reveal the multi-time scale variations in NH3 concentration and to quantify NH3 dry deposition more accurately.

Evaluation of GPM IMERG Satellite Precipitation Products in Event-Based Flood Modeling over the Sunshui River Basin in Southwestern China

This study evaluates the applicability of hourly Global Precipitation Measurement Mission (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) data for event-based flood modeling in the Sunshui River Basin, southwestern China, using the hydrologic modeling system (HEC-HMS) model. The accuracies of IMERG V6, IMERG V7, and the corrected IMERG V7 satellite precipitation products (SPPs) were assessed against ground rainfall observations. The performance of flood modeling based on the original and the corrected SPPs was then evaluated and compared. In addition, the ability of different numbers (one–eight) of ground stations to correct IMERG V7 data for flood modeling was investigated. The results indicate that IMERG V6 data generally underestimate the actual rainfall of the study area, while IMERG V7 and the corrected IMERG V7 data using the geographical discrepancy analysis (GDA) method overestimate rainfall. The corrected IMERG V7 data performed best in capturing the actual rainfall events, followed by IMERG V7 and IMERG V6 data, respectively. The IMERG V7-generated flood hydrographs exhibited the same trend as those of the measured data, yet the former generally overestimated the flood peak due to its overestimation of rainfall. The corrected IMERG V7 data led to superior event-based flood modeling performance compared to the other datasets. Furthermore, when the number of ground stations used to correct the IMERG V7 data in the study area was greater than or equal to four, the flood modeling performance was satisfactory. The results confirm the applicability of IMERG V7 data for fine time scales in event-based flood modeling and reveal that using the GDA method to correct SPPs can greatly enhance the accuracy of flood modeling. This study can act as a basis for flood research in data-scarce areas.

Detection of plant cadmium toxicity by monitoring dielectric response of intact root systems on a fine timescale.

The root dielectric response was measured on a minute scale to assess its efficiency for monitoring short-term cadmium (Cd) toxicity non-destructively. Electrical capacitance (CR), dissipation factor (DR) and electrical conductance (GR) were detected during the 24 to 168 h after Cd treatment (0, 20, 50 mg Cd2+ kg-1 substrate) in potted maize, cucumber and pea. Stress was also evaluated by measuring leaf chlorophyll content, Fv/Fm and stomatal conductance (gs) in situ, and shoot and root mass and total root length after harvest. CR showed a clear diurnal pattern, reflecting the water uptake rate, and decreased significantly in response to excessive Cd due to impeded root growth, the reduced tissue permittivity caused by accelerated lignification, and root ageing. Cd exposure markedly increased DR, indicating greater conductive energy loss due to oxidative membrane damage and enhanced electrolyte leakage. GR, which was coupled with root hydraulic conductance and varied diurnally, was increased transiently by Cd toxicity due to enhanced membrane permeability, but declined thereafter owing to stress-induced leaf senescence and transpiration loss. The time series of impedance components indicated the comparatively high Cd tolerance of the applied maize and the sensitivity of pea cultivar, which was confirmed by visible shoot symptoms, repeated physiological investigations and biomass measurements. The results demonstrated the potential of single-frequency dielectric measurements to follow certain aspects of the stress response of different species on a fine timescale without plant injury. The approach can be combined with widely used plant physiological methods and could contribute to breeding crop genotypes with improved stress tolerance.

Evaluation of Retrospective National Water Model Soil Moisture and Streamflow for Drought‐Monitoring Applications

AbstractThe National Oceanic and Atmospheric Administration (NOAA)’s National Water Model (NWM) provides analyses and predictions of hydrologic variables relevant to drought monitoring and forecasts at fine time and space scales (hourly, 0.25–1 km). We present results exploring the potential for NWM soil moisture and streamflow analyses to inform operational drought monitoring. Both agricultural and hydrologic drought monitoring rely either explicitly or implicitly on an accurate representation of anomalous soil moisture values. Much of our analysis focuses on comparisons of soil moisture anomalies in the NWM to those from in‐situ observations. To establish benchmarks for NWM soil moisture skill, we also include other gridded data sets currently used to inform the US Drought Monitor, specifically those from the North American Land Data Assimilation System phase 2 (NLDAS‐2) land surface models. We then compare NWM streamflow low flows with ∼500 stream gauges from the United States Geological Survey (USGS) Hydro‐Climatic Data Network of undisturbed basins. The NWM soil moisture simulation’s skill parallels that from NLDAS‐2. The accuracy of drought condition identification from NWM streamflow exceeds that based on soil moisture as determined by Critical Success Index scores for extreme dry percentiles. Different meteorological forcings are used in the operational NWM cycles than those used in this retrospective analysis. This forcing disconnect, together with concerns about current‐generation land surface model soil moisture‐transport schemes, inhibit its current operational use for drought monitoring.

Parameterization of the Ångström–Prescott formula based on machine learning benefit estimation of reference crop evapotranspiration with missing solar radiation data

AbstractAccurately estimated reference evapotranspiration (ET0) is essential to regional water management. The FAO recommends coupling the Penman–Monteith (P‐M) model with the Ångström–Prescott (A‐P) formula as the standard method for ET0 estimation with missing Rs measurements. However, its application is usually restricted by the two fundamental coefficients (a and b) of the A‐P formula. This paper proposes a new method for estimating ET0 with missing Rs by combining machine learning with physical‐based P‐M models (PM‐ET0). The benchmark values of the A‐P coefficients were first determined at the daily, monthly, and yearly scales, and further evaluated in Rs and ET0 estimates at 80 national Rs measuring stations. Then, three empirical models and four machine‐learning methods were evaluated in estimating the A‐P coefficients. Machine learning methods were also used to estimate ET0 (ML‐ET0) to compare with the PM‐ET0. Finally, the optimal estimation method was used to estimate the A‐P coefficients for the 839 regular weather stations for ET0 estimation without Rs measurement for China. The results demonstrated a descending trend for coefficient a from northwest to southeast China, with larger values in cold seasons. However, coefficient b showed the opposite distribution as the coefficient a. The FAO has recommended a larger a but a smaller b for southeast China, which produced the region's largest Rs and ET0 estimation errors. Additionally, the A‐P coefficients calibrated at the daily scale obtained the best estimation accuracy for both Rs and ET0, and slightly outperformed the monthly and yearly coefficients without significant difference in most cases. The machine learning methods outperformed the empirical methods for estimating the A‐P coefficients, especially for the sites with extreme values. Further, ML‐ET0 outperformed the PM‐ET0 with yearly A‐P coefficients but underperformed those with daily and monthly ones. This study indicates an exciting potential for combining machine learning with physical models for estimating ET0. However, we found that using the A‐P coefficients with finer time scales is unnecessary to deal with the missing Rs measurements.

Common Sequential Organization of Face Processing in the Human Brain and Convolutional Neural Networks

Face processing includes two crucial processing levels – face detection and face recognition. However, it remains unclear how human brains organize the two processing levels sequentially. While some studies found that faces are recognized as fast as they are detected, others have reported that faces are detected first, followed by recognition. We discriminated the two processing levels on a fine time scale by combining human intracranial EEG (two females, three males, and three subjects without reported sex information) and representation similarity analysis. Our results demonstrate that the human brain exhibits a “detection-first, recognition-later” pattern during face processing. In addition, we used convolutional neural networks to test the hypothesis that the sequential organization of the two face processing levels in the brain reflects computational optimization. Our findings showed that the networks trained on face recognition also exhibited the “detection-first, recognition-later” pattern. Moreover, this sequential organization mechanism developed gradually during the training of the networks and was observed only for correctly predicted images. These findings collectively support the computational account as to why the brain organizes them in this way.

Enhancing the Robustness of Traffic Signal Control with StageLight: A Multiscale Learning Approach

The continuous evolution of artificial intelligence and cyber–physical systems has presented promising opportunities for optimizing traffic signal control in densely populated urban areas, with the aim of alleviating traffic congestion. One area that has garnered significant interest from both researchers and practitioners is the application of deep reinforcement learning (DRL) in traffic signal control. However, DRL-based algorithms often suffer from instability due to the dynamic nature of traffic flows. Discrepancies between the environments used for training and those encountered during deployment often lead to operational failures. Moreover, conventional DRL-based traffic signal control algorithms tend to reveal vulnerabilities when faced with unforeseen events, such as sensor failure. These challenges highlight the need for innovative solutions to enhance the robustness and adaptability of such systems. To address these pertinent issues, this paper introduces StageLight, a novel two-stage multiscale learning approach, which involves learning optimal timings on a coarse time scale in stage 1, while finetuning them on a finer time scale in stage 2. Our experimental results demonstrate StageLight’s remarkable capability to generalize across diverse traffic conditions and its robustness to various sensor-failure scenarios.

Stronger control of surface conductance by soil water content than vapor pressure deficit regulates evapotranspiration in an urban forest in Beijing, 2012–2022

With urbanization rapidly increasing, evapotranspiration (ET) in urban forests plays an increasingly important role in urban hydrology and climate. However, large uncertainty remains regarding the regulating factors of ET in urban area. Using the eddy-covariance technique, we investigated the temporal variations of ET in an urban forest park in Beijing for 2012–2022. Daily ET was close to zero during winter and reached 3–6 mm day−1 in summer. Daily ET increased with vapor pressure deficit (VPD) and soil water content (SWC). When binning daily data into 10 bins according to percentiles of either SWC or VPD, surface conductance (gs) increased with SWC for all VPD bins, but showed weak responses to VPD for SWC bins. Monthly ET increased linearly with normalized difference vegetation index, showed a strong correlation with gs, and exhibited saturated responses to increasing monthly precipitation (PPT). Annual ET ranged from 326 to 566 mm (with the eleven-year mean of 428.66 ± 83.83 mm; mean±SD), and varied from 55 % to 148 % of annual PPT. Aggregated ET over the eleven years (4715 mm) accounted for 82 % of the aggregated PPT. Soil water replenishment through PPT from mid-summer to late autumn of the previous year was responsible for the generally higher monthly ET in spring relative to PPT, resulting in a weak correlation between annual ET and PPT in the same calendar year (R2= 0.28). The PPT in the early growing season partly promoted annual ET through its effect on summer gs. Our results suggest that biotic factors and PPT seasonality played an essential role in regulating ET at the seasonal and interannual scales, respectively. Soil dryness imposed additional constraints on gs at the finer timescale (i.e., days). Urban land-surface models should adequately address the soil moisture carryover effect to improve simulations.

Using detrending to assess SARS-CoV-2 wastewater loads as a leading indicator of fluctuations in COVID-19 cases at fine temporal scales: Correlations across twenty sewersheds in North Carolina

Wastewater surveillance emerged during the COVID-19 pandemic as a novel strategy for tracking the burden of illness in communities. Previous work has shown that trends in wastewater SARS-CoV-2 viral loads correlate well with reported COVID-19 case trends over longer time periods (i.e., months). We used detrending time series to reveal shorter sub-trend patterns (i.e., weeks) to identify leads or lags in the temporal alignment of the wastewater/case relationship. Daily incident COVID-19 cases and twice-weekly wastewater SARS-CoV-2 viral loads measured at 20 North Carolina sewersheds in 2021 were detrended using smoothing ranges of ∞, 16, 8, 4 and 2 weeks, to produce detrended cases and wastewater viral loads at progressively finer time scales. For each sewershed and smoothing range, we calculated the Spearman correlation between the cases and the wastewater viral loads with offsets of -7 to +7 days. We identified a conclusive lead/lag relationship at 15 of 20 sewersheds, with detrended wastewater loads temporally leading detrended COVID-19 cases at 11 of these sites. For the 11 leading sites, the correlation between wastewater loads and cases was greatest for wastewater loads sampled at a median lead time of 6 days before the cases were reported. Distinct lead/lag relationships were the most pronounced after detrending with smoothing ranges of 4–8 weeks, suggesting that SARS-CoV-2 wastewater viral loads can track fluctuations in COVID-19 case incidence rates at fine time scales and may serve as a leading indicator in many settings. These results could help public health officials identify, and deploy timely responses in, areas where cases are increasing faster than the overall pandemic trend.

Changes in surface ozone in South Korea on diurnal to decadal timescales for the period of 2001–2021

Abstract. Several studies have reported an increasing trend of surface ozone in South Korea over the past few decades, using different measurement metrics. In this study, we examined the surface ozone trends in South Korea by analyzing the hourly or daily maximum 8 h average ozone concentrations (MDA8) measured at the surface from 2001 to 2021. We studied the diurnal, seasonal, and multi-decadal variations of these parameters at city, province, and background sites. We found that the fourth-highest MDA8 values exhibited positive trends in seven cities, nine provinces, and two background sites from 2001 to 2021. For the majority of sites, there was an annual increase of approximately 1–2 ppb. After early 2010, all sites consistently recorded MDA8 values exceeding 70 ppb, despite reductions in precursor pollutants such as NO2 and CO. The diurnal and seasonal characteristics of ozone exceedances, defined as the percentage of data points with hourly ozone concentrations exceeding 70 ppb, differed between the Seoul Metropolitan Area (SMA) and the background sites. In the SMA, the exceedances were more prevalent during summer compared to spring, whereas the background sites experienced higher exceedances in spring than in summer. This indicates the efficient local production of ozone in the SMA during summer and the strong influence of long-range transport during spring. The rest of the sites showed similar exceedance patterns during both spring and summer. The peak exceedances occurred around 16:00–17:00 in the SMA and most locations, while the background sites primarily recorded exceedances throughout the night. During the spring of the COVID-19 pandemic (2020–2021), ozone exceedances decreased at most locations, potentially due to significant reductions in NOx emissions in South Korea and China compared to the period of 2010–2019. The largest decreases in exceedances were observed at the background sites during spring. For instance, in Gosung, Gangwondo (approximately 600 m above sea level), the exceedances dropped from 30 % to around 5 % during the COVID-19 pandemic. Regional model simulations confirmed the concept of decreased ozone levels in the boundary layer in Seoul and Gangwon-do in response to emission reductions. However, these reductions in ozone exceedances were not observed in major cities and provinces during the summer of the COVID-19 pandemic, as the decreases in NOx emissions in South Korea and China were much smaller compared to spring. This study highlights the distinctions between spring and summer in the formation and transport of surface ozone in South Korea, emphasizing the importance of monitoring and modeling specific processes for each season or finer timescales.

Multivariate multiscale entropy (mMSE) as a tool for understanding the resting-state EEG signal dynamics: the spatial distribution and sex/gender-related differences

BackgroundThe study aimed to determine how the resting-state EEG (rsEEG) complexity changes both over time and space (channels). The complexity of rsEEG and its sex/gender differences were examined using the multivariate Multiscale Entropy (mMSE) in 95 healthy adults. Following the probability maps (Giacometti et al. in J Neurosci Methods 229:84–96, 2014), channel sets have been identified that correspond to the functional networks. For each channel set the area under curve (AUC), which represents the total complexity, MaxSlope—the maximum complexity change of the EEG signal at thefine scales (1:4 timescales), and AvgEnt—to the average entropy level at coarse-grained scales (9:12 timescales), respectively, were extracted. To check dynamic changes between the entropy level at the fine and coarse-grained scales, the difference in mMSE between the #9 and #4 timescale (DiffEnt) was also calculated.ResultsWe found the highest AUC for the channel sets corresponding to the somatomotor (SMN), dorsolateral network (DAN) and default mode (DMN) whereas the visual network (VN), limbic (LN), and frontoparietal (FPN) network showed the lowest AUC. The largest MaxSlope were in the SMN, DMN, ventral attention network (VAN), LN and FPN, and the smallest in the VN. The SMN and DAN were characterized by the highest and the LN, FPN, and VN by the lowest AvgEnt. The most stable entropy were for the DAN and VN while the LN showed the greatest drop of entropy at the coarse scales. Women, compared to men, showed higher MaxSlope and DiffEnt but lower AvgEnt in all channel sets.ConclusionsNovel results of the present study are: (1) an identification of the mMSE features that capture entropy at the fine and coarse timescales in the channel sets corresponding to the main resting-state networks; (2) the sex/gender differences in these features.

Macroscale composite principal-monotonicity distributed projection of climate-induced hydrologic changes: Tempo-spatial variabilities, and variation decompositions

A macroscale composite principal-monotonicity distributed hydro-projection system is developed through hybridizing advanced statistical hydroclimatic systems analysis methods of verified superiorities. The system can enhance accuracies and feasibilities of climate-informed hydrologic projections, quantitatively reveal climate-induced hydro-magnitude and -variability changes, and decompose variations of the changes with factors such as time, space, scales and greenhouse gas (GHG) emissions. The system of high adaptability for worldwide watersheds is applied to a representative large cold-region watershed, Athabasca River Basin in Canada. Incremental ratios of river discharges with climate change are projected to enlarge at fine timescales, e.g., from 3.4% (octo-decadal) to 7.0% (monthly). According to mixed-level multifactorial analyses, variations of the ratios are dominated by catchments (59.2%) and GHG emissions (19.6%) at annual and longer timescales, and the dominance increases with timescales. At intra-annual timescales, the dominator becomes interactive tempo-spatial variations (62.6%) of the ratios. GHG emissions and their interaction with catchments pose significant or even leading effects on trends of discharge changes. Ratios of changes (i.e., decreases mostly) in tempo-spatial variabilities of discharges are generally the most significant for interannual variabilities, in warm seasons, at downstream catchments, under high GHG emissions, and by the late 21st century. Their variation is dominated by individual factorial effects (78.0%), e.g., catchments (50.3%) and GHG emissions (16.2%); the dominance declines from interannual (89.0%), temporal (79.4%), intra-annual (75.6%) to spatial (67.9%) variabilities. Interactions of the factors in explaining variations of spatial and interannual variabilities are non-negligible in warm and cool seasons, respectively. These findings can help advance macroscale distributed climate-informed hydrologic projections and, accordingly, scientize assessment, adaptation and mitigation of hydrologic hazards over climate-sensitive cold regions.

Assessment and Performance of Flexible Quench Antenna Array Diagnostics for Superconducting Magnets

FNAL has been developing multiple versions of flexible quench antennas (flex-QA), including some specially optimized for high sensitivity and/or high resolution, to characterize quench events and transients during current ramping in superconducting magnets. A fundamental feature in our use of these is the creation of grid-like structures of sensitive elements to cover coil surfaces, with the aim of getting precise localization of magnetic flux-change events. The flex-QA are coupled with fast data-acquisition, allowing comprehensive analysis of signals at the relevant fine time scales. In addition to arrays of various flex-QA types being used during cryogenic testing of superconducting magnets, we also are utilizing a newly developed room temperature test stand to better understand QA response characteristics. The data from actual superconducting magnet tests, “warm” test stand measurements, and simulation data on the same QA designs allows us to draw conclusions on operational feasibility and plan better for improvements of our sensors. In this paper we present data from the multiple tests performed and analysis results. Flex-QA designs are compared, and their features, options, and optimization discussed.

Sustainability of Animal Stocks in Traditional Pastoral Systems of the High-Altitude Andean Altiplano

The main traditional livelihood in the high Central Andes Plateau, locally known as the Puna, is pastoralism. This system is based on llamas (Lama glama) and sheep (Ovis aries), which graze on natural vegetation (shrub steppe, grasslands, and peatlands). So, the livelihood of Andean communities is strongly influenced by the spatial and temporal dynamics of vegetation. Most of the existing models on herbivory used to estimate sustainable livestock loads require information on the nutritional values and primary production of vegetation, which is lacking for the Argentine Puna. This work aimed to fill the gap by implementing a carrying capacity model using data on annual vegetation growth and herbivore feeding strategies collected in the field in different plant communities. The hypothesis that guided this work was that environmental heterogeneity affects management decisions and should be taken into account to model sustainable animal stocks in Andean pastoralist systems. We estimated carrying capacity based on the MSY logistic model, considering biomass and aboveground primary production of vegetation communities, climatic features, and herbivore selective responses. We compared the estimated carrying capacity with real animal stocks in four production units within the study site in 2013, 2014, and 2017. The results indicate that the carrying capacity presents high spatial and temporal variability. In three of the production units assessed, the livestock—mixed herds of sheep and llamas—were above or near the estimated carrying capacity, even when the stocking rates were reduced during dry periods. The high stocking rates, along with evidence of land degradation, may indicate moderate to severe overgrazing. This work demonstrates the usefulness of estimating the carrying capacity to monitor the pastoral systems at fine space and time scales. These monitoring techniques can facilitate decision making among Puna herders in a biocultural context of knowledge dialog that may foster sustainability in pastoralist livelihoods.

Abnormal dynamic functional connectivity of thalamic subregions in patients with first-episode, drug-naïve major depressive disorder.

Recent studies have shown that major depressive disorder (MDD) is associated with altered intrinsic functional connectivity (FC) of the thalamus; however, investigations of these alterations at a finer time scale and the level of thalamic subregions are still lacking. We collected resting-state functional MRI data from 100 treatment-naïve, first-episode MDD patients and 99 age-, gender- and education-matched healthy controls (HCs). Seed-based whole-brain sliding window-based dFC analyses were performed for 16 thalamic subregions. Between-group differences in the mean and variance of dFC were determined using threshold-free cluster enhancement algorithm. For significant alterations, there relationships with clinical and neuropsychological variables were further examined via bivariate and multivariate correlation analyses. Of all thalamic subregions, only the left sensory thalamus (Stha) showed altered variance of dFC in the patients characterized by increases with the left inferior parietal lobule, left superior frontal gyrus, left inferior temporal gyrus, and left precuneus, and decreases with multiple frontal, temporal, parietal, and subcortical regions. These alterations accounted for, to a great extent, clinical, and neuropsychological characteristics of the patients as revealed by the multivariate correlation analysis. In addition, the bivariate correlation analysis revealed a positive correlation between the variance of dFC between the left Stha and right inferior temporal gurus/fusiform and childhood trauma questionnaires scores (r = 0.562, P < 0.001). These findings suggest that the left Stha is the most vulnerable thalamic subregion to MDD, whose dFC alterations may serve as potential biomarkers for the diagnosis of the disease.

Measuring and modeling diel oxygen dynamics in a shallow hypereutrophic estuary: Implications of low oxygen exposure on aquatic life

Hypoxia, or low dissolved oxygen (DO) is a common outcome of excess nitrogen and phosphorus delivered to coastal waterbodies. Shallow and highly productive estuaries are particularly susceptible to diel-cycling hypoxia, which can exhibit DO excursions between anoxia (DO ≤1 mg L−1) and supersaturated concentrations within a day. Shallow estuaries exhibiting diel-cycling hypoxia are understudied relative to larger and deeper estuaries, with very few mechanistic models that can predict diel oxygen dynamics. We utilized continuous monitoring data and the Coastal Generalized Ecosystem Model (CGEM) coupled with an Environmental Fluid Dynamics Code (EFDC) hydrodynamic model to simulate diel DO dynamics in Weeks Bay, AL. Low oxygen conditions ranging from anoxia to DO ≤4 mg L−1 were consistently observed and simulated in the lower water column for periods of minutes to >11 h. High frequency observations and model simulations also identified significant vertical gradients in near bottom DO that varied as much as 0.8 to 3.1 mg L−1 within 0.4 m from the bottom. This spatiotemporal variability presents unique challenges to adequately quantify DO dynamics and the potential exposure of aquatic life to low oxygen conditions. Our results demonstrate the need for detailed measurements to adequately quantify the complex DO dynamics in shallow estuaries. We also demonstrate that simulation models can be successfully applied to evaluate diel oxygen dynamics in complex estuarine environments when calibrated with fine time scale data and effective parameterization of water column and benthic metabolic processes.

Individualized Tracking of Neurocognitive-State-Dependent Eye-Movement Features Using Mobile Devices

With current clinical techniques, it is difficult to assess a patient's neurodegenerative disease (e.g., Alzheimer's) state accurately and frequently. The most widely used tests are qualitative or only performed intermittently, motivating the need for quantitative, accurate, and unobtrusive metrics to track disease progression. Clinical studies have shown that saccade latency (an eye movement measure of reaction time) and error rate (the proportion of eye movements in the wrong direction) may be significantly affected by neurocognitive diseases. Nevertheless, how these features change over time as a disease progresses is underdeveloped due to the constrained recording setup. In this work, our goal is to first understand how these features change over time in healthy individuals. To do so, we used a mobile app to frequently and accurately measure these features outside of the clinical environment from 80 healthy participants. We analyzed their longitudinal characteristics and designed an individualized longitudinal model using a Gaussian process. With a system that can measure eye-movement features on a much finer timescale in a broader population, we acquired a better understanding of eye-movement features from healthy individuals and provided research directions in understanding whether eye-movement features can be used to track neurocognitive states.

An experimental analysis of density dependence in meadow voles: Within-season and delayed effects.

Wild mammal populations exhibit a variety of dynamics, ranging from fairly stable with little change in population size over time to high-amplitude cyclic or erratic fluctuations. A persistent question in population ecology is why populations fluctuate as they do. Answering this seemingly simple question has proven to be challenging. Broadly, density-dependent feedback mechanisms should allow populations to grow at low density and slow or halt growth at high density. However, experimental tests of what demographic processes result in density-dependent feedback and on what timescale have proven elusive. Here, we used replicated density perturbation experiments and capture-mark-recapture analyses to test density-dependent population growth in populations of meadow voles (Microtus pennsylvanicus) during the summer breeding season by manipulating founding population density and observing the pattern of survival, reproduction, and population growth. High population density had no consistent effect on survival rates but generally negatively influenced recruitment and population growth rates. However, these density-dependent effects varied within the breeding season and across years. Our study provides evidence that density-dependent feedback mechanisms operate at finer time scales than previously believed and that process, additively with delayed year effects, is key to understanding multiyear population demography.

Evidence for acoustic niche partitioning depends on the temporal scale in two sympatric Bornean hornbill species

AbstractUnderstanding niche partitioning of closely related sympatric species is a fundamental goal in ecology. Acoustic communication space can be considered a resource, and the acoustic niche hypothesis posits that competition between species may lead to partitioning of communication space. Here, we compare the calling behavior of two sympatric Bornean hornbill species—the rhinoceros hornbill (Buceros rhinoceros) and the helmeted hornbill (Rhinoplax vigil)—to test for evidence of acoustic niche partitioning. Both hornbill species emit calls heard over many kilometers and have similar habitat preferences which is predicted to result in interspecific competition. We collected acoustic data on sympatric populations of both hornbill species using 10 autonomous recording units in Danum Valley Conservation Area, Sabah, Malaysia. We found that there was substantial spectral overlap between the calls of the two species, indicating the potential for competition for acoustic space. To test for evidence of acoustic niche partitioning, we investigated spatial and temporal patterns of calling in each species. Both hornbills were strictly diurnal and called throughout the day, and we were equally likely to detect both species at each of our recorders. We did not find evidence of temporal acoustic avoidance at a relatively coarse timescale when we divided our dataset into 1 h bins, but we did find evidence of temporal acoustic avoidance at a finer timescale when we used null models to compare the observed duration of overlapping calls to the expected amount of overlap due to chance.Abstract in Malay is available with online material.